The invention relates to a pattern recognition method, wherein for an object to be recognized with regard to one or several identifiers from a predetermined set of identifiers, respective numerical, discrete evaluations, each within an evaluation scale, are estimated and wherein, on the basis of these evaluations, a decision is made in a decision stage regarding the allocation of the object to be recognized to an identifier.
Pattern recognition methods are particularly important in automatic control engineering and in machine text processing, for instance, in optical character recognition ("OCR") readers of automatic letter distribution systems or the analysis of forms. Here, one starts from a limited set of identifiers, for example, the alphanumeric character set in different styles plus special characters, and it is the object of the recognition method to allocate an object which is to be recognized to an identifier as reliably as possible.
For this purpose, numeric evaluations are estimated in an estimating stage of a classifier for the object to be recognized with regard to one or several possible identifiers, following thorough preprocessing of the object data; in a subsequent decision stage, these evaluations are used as a basis for the decision regarding the allocation of the object to an identifier.
The allocation decisions are tainted with residual errors resulting from rejections and the acceptance of inapplicable identifiers (substitutions). The desire to accomplish a lower rejection rate and, at the same time, a lower substitution rate means that contradictory demands are placed on the automatic decision-making process.
From DE 41 33 590 A1 a method is known for the classification of signals representing amplitude values, wherein characteristics are extracted from the signals and occurrence probabilities are derived from these. From the entirety of the derived occurrence probabilities, a decision quantity is calculated and compared to a threshold value.
In IEEE Trans. on Electr. Comp., Vol. EC-16, No. 3, June 1967, p. 308-319, a method for the generation of parameters of a polynomial function for a classifier is known, wherein for each category possible during classification a probability density function is estimated such that classification can take place according to a so-called Bayes decision rule.
In IEEE Trans. on Autom. Control, Vol. AC-11, No. 1, January 1966, p. 6-19, adaptive systems for automatic controls having feedback loops to a classifier are described. In this context, the problems of decision theory and stochastic approximation are dealt with in particular. The advantages of trainable adaptive threshold setting are emphasized.